1k citations · 1.1k across the 5 of their papers we have counts for
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cs.CL2018★ 7 cited
Universal Language Model Fine-Tuning with Subword Tokenization for Polish
Piotr Czapla, Jeremy Howard, Marcin Kardas
Universal Language Model for Fine-tuning [arXiv:1801.06146] (ULMFiT) is one of the first NLP methods for efficient inductive transfer learning. Unsupervised pretraining results in…
cs.LG2018★ 12 cited
The Matrix Calculus You Need For Deep Learning
Terence Parr, Jeremy Howard
This paper is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We assume no math knowledge beyond what you learne…
cs.CL2018
Universal Language Model Fine-tuning for Text Classification
Jeremy Howard, Sebastian Ruder
Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Uni…